feat: improvements in test gen

src/ragas/testset/graph.py

@@ -1,6 +1,7 @@
 import json
 import typing as t
 import uuid
+from copy import deepcopy
 from dataclasses import dataclass, field
 from enum import Enum
 from pathlib import Path
@@ -268,6 +269,53 @@ def dfs(node: Node, cluster: t.Set[Node], depth: int, path: t.Tuple[Node, ...]):
 
         return unique_clusters
 
+    def remove_node(
+        self, node: Node, inplace: bool = True
+    ) -> t.Optional["KnowledgeGraph"]:
+        """
+        Removes a node and its associated relationships from the knowledge graph.
+
+        Parameters
+        ----------
+        node : Node
+            The node to be removed from the knowledge graph.
+        inplace : bool, optional
+            If True, modifies the knowledge graph in place.
+            If False, returns a modified copy with the node removed.
+
+        Returns
+        -------
+        KnowledgeGraph or None
+            Returns a modified copy of the knowledge graph if `inplace` is False.
+            Returns None if `inplace` is True.
+
+        Raises
+        ------
+        ValueError
+            If the node is not present in the knowledge graph.
+        """
+        if node not in self.nodes:
+            raise ValueError("Node is not present in the knowledge graph.")
+
+        if inplace:
+            # Modify the current instance
+            self.nodes.remove(node)
+            self.relationships = [
+                rel
+                for rel in self.relationships
+                if rel.source != node and rel.target != node
+            ]
+        else:
+            # Create a deep copy and modify it
+            new_graph = deepcopy(self)
+            new_graph.nodes.remove(node)
+            new_graph.relationships = [
+                rel
+                for rel in new_graph.relationships
+                if rel.source != node and rel.target != node
+            ]
+            return new_graph
+
     def find_direct_clusters(
         self, relationship_condition: t.Callable[[Relationship], bool] = lambda _: True
     ) -> t.Dict[Node, t.List[t.Set[Node]]]:

src/ragas/testset/graph_queries.py

@@ -36,3 +36,38 @@ def dfs(current_node: Node, current_level: int):
     dfs(node, 1)
 
     return children
+
+
+def get_parent_nodes(node: Node, graph: KnowledgeGraph, level: int = 1) -> t.List[Node]:
+    """
+    Get the parent nodes of a given node up to a specified level.
+
+    Parameters
+    ----------
+    node : Node
+        The node to get the parents of.
+    graph : KnowledgeGraph
+        The knowledge graph containing the node.
+    level : int
+        The maximum level to which parent nodes are searched.
+
+    Returns
+    -------
+    List[Node]
+        The list of parent nodes up to the specified level.
+    """
+    parents = []
+
+    # Helper function to perform depth-limited search for parent nodes
+    def dfs(current_node: Node, current_level: int):
+        if current_level > level:
+            return
+        for rel in graph.relationships:
+            if rel.target == current_node and rel.type == "child":
+                parents.append(rel.source)
+                dfs(rel.source, current_level + 1)
+
+    # Start DFS from the initial node at level 0
+    dfs(node, 1)
+
+    return parents

src/ragas/testset/transforms/__init__.py

@@ -1,4 +1,4 @@
-from .base import BaseGraphTransformation, Extractor, RelationshipBuilder, Splitter
+from .base import BaseGraphTransformation, Extractor, RelationshipBuilder, Splitter, NodeFilter
 from .default import default_transforms
 from .engine import Parallel, Transforms, apply_transforms, rollback_transforms
 from .extractors import (
@@ -13,6 +13,7 @@
     SummaryCosineSimilarityBuilder,
 )
 from .splitters import HeadlineSplitter
+from .filters import CustomNodeFilter
 
 __all__ = [
     # base
@@ -37,4 +38,6 @@
     "SummaryCosineSimilarityBuilder",
     # splitters
     "HeadlineSplitter",
+    "CustomNodeFilter",
+    "NodeFilter",
 ]

src/ragas/testset/transforms/base.py

@@ -322,3 +322,55 @@ async def apply_build_relationships(
 
         filtered_kg = self.filter(kg)
         return [apply_build_relationships(filtered_kg=filtered_kg, original_kg=kg)]
+
+
+@dataclass
+class NodeFilter(BaseGraphTransformation):
+
+    async def transform(self, kg: KnowledgeGraph) -> KnowledgeGraph:
+
+        filtered = self.filter(kg)
+
+        for node in filtered.nodes:
+            flag = await self.custom_filter(node, kg)
+            if flag:
+                kg_ = kg.remove_node(node, inplace=False)
+                if isinstance(kg_, KnowledgeGraph):
+                    return kg_
+                else:
+                    raise ValueError("Error in removing node")
+        return kg
+
+    @abstractmethod
+    async def custom_filter(self, node: Node, kg: KnowledgeGraph) -> bool:
+        """
+        Abstract method to filter a node based on a prompt.
+
+        Parameters
+        ----------
+        node : Node
+            The node to be filtered.
+
+        Returns
+        -------
+        bool
+            A boolean indicating whether the node should be filtered.
+        """
+        pass
+
+    def generate_execution_plan(self, kg: KnowledgeGraph) -> t.List[t.Coroutine]:
+        """
+        Generates a list of coroutines to be executed
+        """
+
+        async def apply_filter(node: Node):
+            if await self.custom_filter(node, kg):
+                kg.remove_node(node)
+
+        filtered = self.filter(kg)
+        return [apply_filter(node) for node in filtered.nodes]
+
+
+@dataclass
+class LLMBasedNodeFilter(NodeFilter, PromptMixin):
+    llm: BaseRagasLLM = field(default_factory=llm_factory)

src/ragas/testset/transforms/default.py

@@ -9,6 +9,7 @@
     SummaryExtractor,
 )
 from ragas.testset.transforms.extractors.llm_based import NERExtractor, ThemesExtractor
+from ragas.testset.transforms.filters import CustomNodeFilter
 from ragas.testset.transforms.relationship_builders import (
     CosineSimilarityBuilder,
     OverlapScoreBuilder,
@@ -82,11 +83,14 @@ def summary_filter(node):
         threshold=0.01, filter_nodes=lambda node: node.type == NodeType.CHUNK
     )
 
+    node_filter = CustomNodeFilter(llm=llm, filter_nodes=lambda node: node.type == NodeType.CHUNK)
+
     transforms = [
         headline_extractor,
         splitter,
-        Parallel(summary_extractor, theme_extractor, ner_extractor),
-        summary_emb_extractor,
+        summary_extractor,
+        node_filter,
+        Parallel(summary_emb_extractor, theme_extractor, ner_extractor),
         Parallel(cosine_sim_builder, ner_overlap_sim),
     ]
 

src/ragas/testset/transforms/extractors/llm_based.py

@@ -70,35 +70,44 @@ class Headlines(BaseModel):
 
 
 class HeadlinesExtractorPrompt(PydanticPrompt[StringIO, Headlines]):
-    instruction: str = "Extract only level 2 headings from the given text."
+    instruction: str = "Extract only level 2 and level 3 headings from the given text."
 
     input_model: t.Type[StringIO] = StringIO
     output_model: t.Type[Headlines] = Headlines
     examples: t.List[t.Tuple[StringIO, Headlines]] = [
         (
             StringIO(
                 text="""\
-        Introduction
-        Overview of the topic...
+                Introduction
+                Overview of the topic...
 
-        Main Concepts
-        Explanation of core ideas...
+                Main Concepts
+                Explanation of core ideas...
 
-        Detailed Analysis
-        Techniques and methods for analysis...
+                Detailed Analysis
+                Techniques and methods for analysis...
 
-        Subsection: Specialized Techniques
-        Further details on specialized techniques...
+                Subsection: Specialized Techniques
+                Further details on specialized techniques...
 
-        Future Directions
-        Insights into upcoming trends...
+                Future Directions
+                Insights into upcoming trends...
 
-        Conclusion
-        Final remarks and summary.
-        """,
+                Subsection: Next Steps in Research
+                Discussion of new areas of study...
+
+                Conclusion
+                Final remarks and summary.
+                """
             ),
             Headlines(
-                headlines=["Main Concepts", "Detailed Analysis", "Future Directions"]
+                headlines=[
+                    "Main Concepts",
+                    "Detailed Analysis",
+                    "Subsection: Specialized Techniques",
+                    "Future Directions",
+                    "Subsection: Next Steps in Research",
+                ]
             ),
         ),
     ]
@@ -108,15 +117,24 @@ class NEROutput(BaseModel):
     entities: t.List[str]
 
 
-class NERPrompt(PydanticPrompt[StringIO, NEROutput]):
-    instruction: str = "Extract named entities from the given text."
-    input_model: t.Type[StringIO] = StringIO
+class TextWithExtractionLimit(BaseModel):
+    text: str
+    max_num: int = 10
+
+
+class NERPrompt(PydanticPrompt[TextWithExtractionLimit, NEROutput]):
+    instruction: str = (
+        "Extract the named entities from the given text, limiting the output to the top entities. "
+        "Ensure the number of entities does not exceed the specified maximum."
+    )
+    input_model: t.Type[TextWithExtractionLimit] = TextWithExtractionLimit
     output_model: t.Type[NEROutput] = NEROutput
-    examples: t.List[t.Tuple[StringIO, NEROutput]] = [
+    examples: t.List[t.Tuple[TextWithExtractionLimit, NEROutput]] = [
         (
-            StringIO(
+            TextWithExtractionLimit(
                 text="""Elon Musk, the CEO of Tesla and SpaceX, announced plans to expand operations to new locations in Europe and Asia.
-                This expansion is expected to create thousands of jobs, particularly in cities like Berlin and Shanghai."""
+                This expansion is expected to create thousands of jobs, particularly in cities like Berlin and Shanghai.""",
+                max_num=10,
             ),
             NEROutput(
                 entities=[
@@ -246,12 +264,16 @@ class NERExtractor(LLMBasedExtractor):
 
     property_name: str = "entities"
     prompt: NERPrompt = NERPrompt()
+    max_num_entities: int = 10
 
     async def extract(self, node: Node) -> t.Tuple[str, t.List[str]]:
         node_text = node.get_property("page_content")
         if node_text is None:
             return self.property_name, []
-        result = await self.prompt.generate(self.llm, data=StringIO(text=node_text))
+        result = await self.prompt.generate(
+            self.llm,
+            data=TextWithExtractionLimit(text=node_text, max_num=self.max_num_entities),
+        )
         return self.property_name, result.entities
 
 
@@ -305,14 +327,17 @@ class ThemesAndConcepts(BaseModel):
     output: t.List[str]
 
 
-class ThemesAndConceptsExtractorPrompt(PydanticPrompt[StringIO, ThemesAndConcepts]):
+class ThemesAndConceptsExtractorPrompt(
+    PydanticPrompt[TextWithExtractionLimit, ThemesAndConcepts]
+):
     instruction: str = "Extract the main themes and concepts from the given text."
-    input_model: t.Type[StringIO] = StringIO
+    input_model: t.Type[TextWithExtractionLimit] = TextWithExtractionLimit
     output_model: t.Type[ThemesAndConcepts] = ThemesAndConcepts
-    examples: t.List[t.Tuple[StringIO, ThemesAndConcepts]] = [
+    examples: t.List[t.Tuple[TextWithExtractionLimit, ThemesAndConcepts]] = [
         (
-            StringIO(
-                text="Artificial intelligence is transforming industries by automating tasks requiring human intelligence. AI analyzes vast data quickly and accurately, driving innovations like self-driving cars and personalized recommendations."
+            TextWithExtractionLimit(
+                text="Artificial intelligence is transforming industries by automating tasks requiring human intelligence. AI analyzes vast data quickly and accurately, driving innovations like self-driving cars and personalized recommendations.",
+                max_num=10,
             ),
             ThemesAndConcepts(
                 output=[
@@ -343,10 +368,14 @@ class ThemesExtractor(LLMBasedExtractor):
 
     property_name: str = "themes"
     prompt: ThemesAndConceptsExtractorPrompt = ThemesAndConceptsExtractorPrompt()
+    max_num_themes: int = 10
 
     async def extract(self, node: Node) -> t.Tuple[str, t.List[str]]:
         node_text = node.get_property("page_content")
         if node_text is None:
             return self.property_name, []
-        result = await self.prompt.generate(self.llm, data=StringIO(text=node_text))
+        result = await self.prompt.generate(
+            self.llm,
+            data=TextWithExtractionLimit(text=node_text, max_num=self.max_num_themes),
+        )
         return self.property_name, result.output